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Supercapacitors have gained increased attention in recent years due to their significant role in energy storage devices; their impact largely depends on the electrode material. The diversity of energy storage mechanisms means that various electrode materials can provide unique benefits for specific applications, highlighting the growing trend towards nanocomposite electrodes. Typically, these nanocomposite electrodes combine pseudocapacitive materials with carbon-based materials to form heterogeneous structural composites, often requiring complex multi-step preparation processes. This study introduces a straightforward approach to fabricate a non-carbon-based Mo@MoO2 nanosheet composite electrode using a one-step thermal evaporating vapor deposition (TEVD) method. This novel electrode features Mo at the core and MoO2 as the shell and demonstrates exceptional electrochemical performance. Specifically, at a current density of 1 A g−1, it achieves a storage capacity of 205.1 F g−1, maintaining virtually unchanged capacity after 10,000 charge–discharge cycles at 2 A g−1. The outstanding long-cycle stability is ascribed to the vertical two-dimensional geometry, the superior conductivity, and pseudocapacitance of the Mo@MoO2 core-shell nanosheets. These attributes significantly improve the electrode’s charge storage capacity, charge transfer speed, and structural integrity during the cycling process. The development of the one-step grown Mo@MoO2 nanosheets offers a promising way for the advancement of high-performance, non-carbon-based supercapacitor nanocomposite electrodes.

Peanut (Arachis hypogaea L.) is an important crop that provides essential proteins and oils for human and animal consumption. 9-cis-epoxycarotenoid dioxygenase (NCED) have been found can play a vital role in abscisic acid (ABA) biosynthesis and may be a response to drought stress. Until now, in Arachis hypogaea, no information about the NCED gene family has been reported and the importance of NCED-related drought tolerance is unclear. In this study, eight NCED genes in Arachis hypogaea, referred to as AhNCEDs, are distributed across eight chromosomes, with duplication events in AhNCED1 and AhNCED2, AhNCED3 and AhNCED4, and AhNCED6 and AhNCED7. Comparative analysis revealed that NCED genes are highly conserved among plant species, including Pisum sativum, Phaseolus vulgaris, Glycine max, Arabidopsis thaliana, Gossypium hirsutum, and Oryza sativa. Further promoter analysis showed AhNCEDs have ABA-related and drought-inducible elements. The phenotyping of Arachis hypogaea cultivars NH5 and FH18 demonstrated that NH5 is drought-tolerant and FH18 is drought-sensitive. Transcriptome expression analysis revealed the differential regulation of AhNCEDs expression in both NH5 and FH18 cultivars under drought stress. Furthermore, compared to the Arachis hypogaea cultivar FH18, the NH5 exhibited a significant upregulation of AhNCED1/2 expression under drought. To sum up, this study provides an insight into the drought-related AhNCED genes, screened out the potential candidates to regulate drought tolerance and ABA biosynthesis in Arachis hypogaea.

Currently, the field of transparent image analysis has gradually become a hot topic. However, traditional analysis methods are accompanied by large amounts of carbon emissions, and consumes significant manpower and material resources. The continuous development of computer vision enables the use of computers to analyze images. However, the low contrast between the foreground and background of transparent images makes their segmentation difficult for computers. To address this problem, we first analyzed them with pixel patches, and then classified the patches as foreground and background. Finally, the segmentation of the transparent images was completed through the reconstruction of pixel patches. To understand the performance of different deep learning networks in transparent image segmentation, we conducted a series of comparative experiments using patch-level and pixel-level methods. In two sets of experiments, we compared the segmentation performance of four convolutional neural network (CNN) models and a visual transformer (ViT) model on the transparent environmental microorganism dataset fifth version. The results demonstrated that U-Net++ had the highest accuracy rate of 95.32% in the pixel-level segmentation experiment followed by ViT with an accuracy rate of 95.31%. However, ResNet50 had the highest accuracy rate of 90.00% and ViT had the lowest accuracy of 89.25% in the patch-level segmentation experiments. Hence, we concluded that ViT performed the lowest in patch-level segmentation experiments, but outperformed most CNNs in pixel-level segmentation. Further, we combined patch-level and pixel-level segmentation results to reduce the loss of segmentation details in the EM images. This conclusion was also verified by the environmental microorganism dataset sixth version dataset (EMDS-6).

Supercapacitors have been extensively studied due to their advantages of fast-charging and discharging, high-power density, long-cycling life, low cost, etc. Exploring novel nanomaterial schemes for high-performance electrode materials is of great significance. Herein, a strategy to combine vertical graphene (VG) with MoO3 nanosheets to form a composite VG/MoO3 nanostructure is proposed. VGs as transition layers supply rich active sites for the growth of MoO3 nanosheets with increasing specific surface areas. The VG transition layer further improves the electric contact and adhesion of the MoO3 electrode, simultaneously stabilizing its volume and crystal structure during repeated redox reactions. Thus, the prepared VG/MoO3 nanosheets have been demonstrated to exhibit excellent electrochemical properties, such as high reversible capacitance, better cycling performance, and high-rate capability.

Pyroptosis is one special type of lytic programmed cell death, featured in cell swelling, rupture, secretion of cell contents and remarkable proinflammation effect. In the process of pyroptosis, danger signalling and cellular events are detected by inflammasome, activating caspases and cleaving Gasdermin D (GSDMD), along with the secretion of IL-18 and IL-1β. Pyroptosis can be divided into canonical pathway and non-canonical pathway, and NLRP3 inflammasome is the most important initiator. Diabetic kidney disease (DKD) is one of the most serious microvascular complications in diabetes. Current evidence reported the stimulatory role of hyperglycaemia-induced cellular stress in renal cell pyroptosis, and different signalling pathways have been shown to regulate pyroptosis initiation. Additionally, the inflammation and cellular injury caused by pyroptosis are tightly implicated in DKD progression, aggravating renal fibrosis, glomerular sclerosis and tubular injury. Some registered hypoglycaemia agents exert suppressive activity in pyroptosis regulation pathway. Latest studies also reported some potential approaches to target the pyroptosis pathway, which effectively inhibits renal cell pyroptosis and alleviates DKD in in vivo or in vitro models. Therefore, comprehensively compiling the information associated with pyroptosis regulation in DKD is the main aim of this review, and we try to provide new insights for researchers to dig out more potential therapies of DKD.